






import argparse
import json
import os
from collections import OrderedDict

import torch

from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken





@torch.no_grad()
def  _lowerCamelCase						(						__lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase       ) ->			int:

     '''simple docstring'''
     # Load configuration defined in the metadata file
     with open(__lowerCamelCase       ) as metadata_file:
          UpperCAmelCase__      :  Union[str, Any]				      =			json.load(__lowerCamelCase       )
     UpperCAmelCase__      :  Optional[Any]				      =			LukeConfig(use_entity_aware_attention=__lowerCamelCase ,							**metadata["""model_config"""]       )

     # Load in the weights from the checkpoint_path
     UpperCAmelCase__      :  Optional[int]				      =			torch.load(__lowerCamelCase ,							map_location="""cpu"""       )["""module"""]

     # Load the entity vocab file
     UpperCAmelCase__      :  List[str]				      =			load_original_entity_vocab(__lowerCamelCase       )
     # add an entry for [MASK2]
     UpperCAmelCase__      :  Any				      =			max(entity_vocab.values()       ) + 1
     config.entity_vocab_size += 1

     UpperCAmelCase__      :  Any				      =			XLMRobertaTokenizer.from_pretrained(metadata["""model_config"""]["""bert_model_name"""]       )

     # Add special tokens to the token vocabulary for downstream tasks
     UpperCAmelCase__      :  str				      =			AddedToken("""<ent>""" ,							lstrip=__lowerCamelCase ,							rstrip=__lowerCamelCase       )
     UpperCAmelCase__      :  int				      =			AddedToken("""<ent2>""" ,							lstrip=__lowerCamelCase ,							rstrip=__lowerCamelCase       )
     tokenizer.add_special_tokens({"""additional_special_tokens""": [entity_token_a, entity_token_a]}       )
     config.vocab_size += 2

     print(F"Saving tokenizer to {pytorch_dump_folder_path}"       )
     tokenizer.save_pretrained(__lowerCamelCase       )
     with open(os.path.join(__lowerCamelCase ,							"""tokenizer_config.json"""       ) ,							"""r"""       ) as f:
          UpperCAmelCase__      :  str				      =			json.load(__lowerCamelCase       )
     UpperCAmelCase__      :  Optional[int]				      =			"""MLukeTokenizer"""
     with open(os.path.join(__lowerCamelCase ,							"""tokenizer_config.json"""       ) ,							"""w"""       ) as f:
          json.dump(__lowerCamelCase ,							__lowerCamelCase       )

     with open(os.path.join(__lowerCamelCase ,							MLukeTokenizer.vocab_files_names["""entity_vocab_file"""]       ) ,							"""w"""       ) as f:
          json.dump(__lowerCamelCase ,							__lowerCamelCase       )

     UpperCAmelCase__      :  str				      =			MLukeTokenizer.from_pretrained(__lowerCamelCase       )

     # Initialize the embeddings of the special tokens
     UpperCAmelCase__      :  List[str]				      =			tokenizer.convert_tokens_to_ids(["""@"""]       )[0]
     UpperCAmelCase__      :  int				      =			tokenizer.convert_tokens_to_ids(["""#"""]       )[0]

     UpperCAmelCase__      :  Dict				      =			state_dict["""embeddings.word_embeddings.weight"""]
     UpperCAmelCase__      :  Tuple				      =			word_emb[ent_init_index].unsqueeze(0       )
     UpperCAmelCase__      :  str				      =			word_emb[enta_init_index].unsqueeze(0       )
     UpperCAmelCase__      :  List[Any]				      =			torch.cat([word_emb, ent_emb, enta_emb]       )
     # add special tokens for 'entity_predictions.bias'
     for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
          UpperCAmelCase__      :  Dict				      =			state_dict[bias_name]
          UpperCAmelCase__      :  List[Any]				      =			decoder_bias[ent_init_index].unsqueeze(0       )
          UpperCAmelCase__      :  Optional[Any]				      =			decoder_bias[enta_init_index].unsqueeze(0       )
          UpperCAmelCase__      :  Dict				      =			torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias]       )

     # Initialize the query layers of the entity-aware self-attention mechanism
     for layer_index in range(config.num_hidden_layers       ):
          for matrix_name in ["query.weight", "query.bias"]:
               UpperCAmelCase__      :  Tuple				      =			F"encoder.layer.{layer_index}.attention.self."
               UpperCAmelCase__      :  Optional[int]				      =			state_dict[prefix + matrix_name]
               UpperCAmelCase__      :  List[str]				      =			state_dict[prefix + matrix_name]
               UpperCAmelCase__      :  Any				      =			state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
     UpperCAmelCase__      :  Any				      =			state_dict["""entity_embeddings.entity_embeddings.weight"""]
     UpperCAmelCase__      :  List[str]				      =			entity_emb[entity_vocab["""[MASK]"""]].unsqueeze(0       )
     UpperCAmelCase__      :  Tuple				      =			torch.cat([entity_emb, entity_mask_emb]       )
     # add [MASK2] for 'entity_predictions.bias'
     UpperCAmelCase__      :  List[str]				      =			state_dict["""entity_predictions.bias"""]
     UpperCAmelCase__      :  int				      =			entity_prediction_bias[entity_vocab["""[MASK]"""]].unsqueeze(0       )
     UpperCAmelCase__      :  str				      =			torch.cat([entity_prediction_bias, entity_mask_bias]       )

     UpperCAmelCase__      :  int				      =			LukeForMaskedLM(config=__lowerCamelCase       ).eval()

     state_dict.pop("""entity_predictions.decoder.weight"""       )
     state_dict.pop("""lm_head.decoder.weight"""       )
     state_dict.pop("""lm_head.decoder.bias"""       )
     UpperCAmelCase__      :  str				      =			OrderedDict()
     for key, value in state_dict.items():
          if not (key.startswith("""lm_head"""       ) or key.startswith("""entity_predictions"""       )):
               UpperCAmelCase__      :  str				      =			state_dict[key]
          else:
               UpperCAmelCase__      :  List[Any]				      =			state_dict[key]

     UpperCAmelCase__      ,		UpperCAmelCase__      :  Any				      =			model.load_state_dict(__lowerCamelCase ,							strict=__lowerCamelCase       )

     if set(__lowerCamelCase       ) != {"luke.embeddings.position_ids"}:
          raise ValueError(F"Unexpected unexpected_keys: {unexpected_keys}"       )
     if set(__lowerCamelCase       ) != {
         "lm_head.decoder.weight",
         "lm_head.decoder.bias",
         "entity_predictions.decoder.weight",
     }:
          raise ValueError(F"Unexpected missing_keys: {missing_keys}"       )

     model.tie_weights()
     assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
     assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()

     # Check outputs
     UpperCAmelCase__      :  Tuple				      =			MLukeTokenizer.from_pretrained(__lowerCamelCase ,							task="""entity_classification"""       )

     UpperCAmelCase__      :  Optional[Any]				      =			"""ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan)."""
     UpperCAmelCase__      :  Union[str, Any]				      =			(0, 9)
     UpperCAmelCase__      :  Union[str, Any]				      =			tokenizer(__lowerCamelCase ,							entity_spans=[span] ,							return_tensors="""pt"""       )

     UpperCAmelCase__      :  Optional[int]				      =			model(**__lowerCamelCase       )

     # Verify word hidden states
     if model_size == "large":
          raise NotImplementedError
     else:  # base
          UpperCAmelCase__      :  Union[str, Any]				      =			torch.Size((1, 33, 768)       )
          UpperCAmelCase__      :  Tuple				      =			torch.tensor([[0.0_892, 0.0_596, -0.2_819], [0.0_134, 0.1_199, 0.0_573], [-0.0_169, 0.0_927, 0.0_644]]       )

     if not (outputs.last_hidden_state.shape == expected_shape):
          raise ValueError(
              F"Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}"       )
     if not torch.allclose(outputs.last_hidden_state[0, :3, :3] ,							__lowerCamelCase ,							atol=1E-4       ):
          raise ValueError

     # Verify entity hidden states
     if model_size == "large":
          raise NotImplementedError
     else:  # base
          UpperCAmelCase__      :  Optional[int]				      =			torch.Size((1, 1, 768)       )
          UpperCAmelCase__      :  List[str]				      =			torch.tensor([[-0.1_482, 0.0_609, 0.0_322]]       )

     if not (outputs.entity_last_hidden_state.shape == expected_shape):
          raise ValueError(
              F"Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is"
              F" {expected_shape}"       )
     if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3] ,							__lowerCamelCase ,							atol=1E-4       ):
          raise ValueError

     # Verify masked word/entity prediction
     UpperCAmelCase__      :  Tuple				      =			MLukeTokenizer.from_pretrained(__lowerCamelCase       )
     UpperCAmelCase__      :  Optional[Any]				      =			"""Tokyo is the capital of <mask>."""
     UpperCAmelCase__      :  int				      =			(24, 30)
     UpperCAmelCase__      :  List[Any]				      =			tokenizer(__lowerCamelCase ,							entity_spans=[span] ,							return_tensors="""pt"""       )

     UpperCAmelCase__      :  Optional[int]				      =			model(**__lowerCamelCase       )

     UpperCAmelCase__      :  Optional[Any]				      =			encoding["""input_ids"""][0].tolist()
     UpperCAmelCase__      :  Optional[int]				      =			input_ids.index(tokenizer.convert_tokens_to_ids("""<mask>"""       )       )
     UpperCAmelCase__      :  Dict				      =			outputs.logits[0][mask_position_id].argmax(dim=-1       )
     assert "Japan" == tokenizer.decode(__lowerCamelCase       )

     UpperCAmelCase__      :  Optional[Any]				      =			outputs.entity_logits[0][0].argmax().item()
     UpperCAmelCase__      :  Union[str, Any]				      =			[
         entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
     ]
     assert [e for e in multilingual_predicted_entities if e.startswith("""en:"""       )][0] == "en:Japan"

     # Finally, save our PyTorch model and tokenizer
     print("""Saving PyTorch model to {}""".format(__lowerCamelCase       )       )
     model.save_pretrained(__lowerCamelCase       )





def  _lowerCamelCase						(						__lowerCamelCase       ) ->			List[Any]:

     '''simple docstring'''
     UpperCAmelCase__      :  List[Any]				      =			["""[MASK]""", """[PAD]""", """[UNK]"""]

     UpperCAmelCase__      :  str				      =			[json.loads(__lowerCamelCase       ) for line in open(__lowerCamelCase       )]

     UpperCAmelCase__      :  Optional[Any]				      =			{}
     for entry in data:
          UpperCAmelCase__      :  Optional[Any]				      =			entry["""id"""]
          for entity_name, language in entry["entities"]:
               if entity_name in SPECIAL_TOKENS:
                    UpperCAmelCase__      :  Union[str, Any]				      =			entity_id
                    break
               UpperCAmelCase__      :  int				      =			F"{language}:{entity_name}"
               UpperCAmelCase__      :  str				      =			entity_id
     return new_mapping


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE__		:							Dict 				=				argparse.ArgumentParser()
      # Required parameters
      parser.add_argument("""--checkpoint_path""", type=str, help="""Path to a pytorch_model.bin file.""")
      parser.add_argument(
          """--metadata_path""", default=None, type=str, help="""Path to a metadata.json file, defining the configuration."""
      )
      parser.add_argument(
          """--entity_vocab_path""",
          default=None,
          type=str,
          help="""Path to an entity_vocab.tsv file, containing the entity vocabulary.""",
      )
      parser.add_argument(
          """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to where to dump the output PyTorch model."""
      )
      parser.add_argument(
          """--model_size""", default="""base""", type=str, choices=["""base""", """large"""], help="""Size of the model to be converted."""
      )
      SCREAMING_SNAKE_CASE__		:							Union[str, Any] 				=				parser.parse_args()
      convert_luke_checkpoint(
          args.checkpoint_path,
          args.metadata_path,
          args.entity_vocab_path,
          args.pytorch_dump_folder_path,
          args.model_size,
      )







# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

_SCREAMING_SNAKE_CASE 		=      """tiny-wmt19-en-ru"""

# Build

# borrowed from a test
_SCREAMING_SNAKE_CASE 		=      [
    """l""",
    """o""",
    """w""",
    """e""",
    """r""",
    """s""",
    """t""",
    """i""",
    """d""",
    """n""",
    """w</w>""",
    """r</w>""",
    """t</w>""",
    """lo""",
    """low""",
    """er</w>""",
    """low</w>""",
    """lowest</w>""",
    """newer</w>""",
    """wider</w>""",
    """<unk>""",
]
_SCREAMING_SNAKE_CASE 		=      dict(zip(vocab, range(len(vocab))))
_SCREAMING_SNAKE_CASE 		=      ["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

with tempfile.TemporaryDirectory() as tmpdirname:
     _SCREAMING_SNAKE_CASE 		=      Path(tmpdirname)
     _SCREAMING_SNAKE_CASE 		=      build_dir / VOCAB_FILES_NAMES["""src_vocab_file"""]
     _SCREAMING_SNAKE_CASE 		=      build_dir / VOCAB_FILES_NAMES["""tgt_vocab_file"""]
     _SCREAMING_SNAKE_CASE 		=      build_dir / VOCAB_FILES_NAMES["""merges_file"""]
     with open(src_vocab_file, """w""") as fp:
          fp.write(json.dumps(vocab_tokens))
     with open(tgt_vocab_file, """w""") as fp:
          fp.write(json.dumps(vocab_tokens))
     with open(merges_file, """w""") as fp:
          fp.write("""\n""".join(merges))

     _SCREAMING_SNAKE_CASE 		=      FSMTTokenizer(
         langs=["""en""", """ru"""],
         src_vocab_size=len(vocab),
         tgt_vocab_size=len(vocab),
         src_vocab_file=src_vocab_file,
         tgt_vocab_file=tgt_vocab_file,
         merges_file=merges_file,
     )

_SCREAMING_SNAKE_CASE 		=      FSMTConfig(
    langs=["""ru""", """en"""],
    src_vocab_size=1_0_0_0,
    tgt_vocab_size=1_0_0_0,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

_SCREAMING_SNAKE_CASE 		=      FSMTForConditionalGeneration(config)
print(F'''num of params {tiny_model.num_parameters()}''')

# Test
_SCREAMING_SNAKE_CASE 		=      tokenizer(["""Making tiny model"""], return_tensors="""pt""")
_SCREAMING_SNAKE_CASE 		=      tiny_model(**batch)

print("""test output:""", len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F'''Generated {mname_tiny}''')

# Upload
# transformers-cli upload tiny-wmt19-en-ru
